Practical applications are more likely to be a hybrid of classical and quantum operations since the currently available quantum computers are only small to intermediate scale. Under this hybrid paradigm, most of the calculations are carried out on classical computers, while the quantum devices are deployed to solve the computational tasks for which certain quantum advantages can be demonstrated. This hybrid approach is not too different from what has been done in the past decade with GPUs. Thus, the main idea behind Quantum Machine Learning (QML) [1]}, [2]} is to replace parts of a neural network (e.g. linear layers) with a quantum counterpart. A comparison between different learning models is shown in Tab. REF .
